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| <div class="section" id="linearregressionwithsgd"> |
| <h1>LinearRegressionWithSGD<a class="headerlink" href="#linearregressionwithsgd" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.regression.LinearRegressionWithSGD"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.regression.</code><code class="sig-name descname">LinearRegressionWithSGD</code><a class="reference internal" href="../../_modules/pyspark/mllib/regression.html#LinearRegressionWithSGD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.regression.LinearRegressionWithSGD" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Train a linear regression model with no regularization using Stochastic Gradient Descent.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| <div class="deprecated"> |
| <p><span class="versionmodified deprecated">Deprecated since version 2.0.0: </span>Use <a class="reference internal" href="pyspark.ml.regression.LinearRegression.html#pyspark.ml.regression.LinearRegression" title="pyspark.ml.regression.LinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.ml.regression.LinearRegression</span></code></a>.</p> |
| </div> |
| <p class="rubric">Methods</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.regression.LinearRegressionWithSGD.train" title="pyspark.mllib.regression.LinearRegressionWithSGD.train"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train</span></code></a>(data[, iterations, step, …])</p></td> |
| <td><p>Train a linear regression model using Stochastic Gradient Descent (SGD).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.regression.LinearRegressionWithSGD.train"> |
| <em class="property">classmethod </em><code class="sig-name descname">train</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">iterations</span><span class="o">=</span><span class="default_value">100</span></em>, <em class="sig-param"><span class="n">step</span><span class="o">=</span><span class="default_value">1.0</span></em>, <em class="sig-param"><span class="n">miniBatchFraction</span><span class="o">=</span><span class="default_value">1.0</span></em>, <em class="sig-param"><span class="n">initialWeights</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">regParam</span><span class="o">=</span><span class="default_value">0.0</span></em>, <em class="sig-param"><span class="n">regType</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">intercept</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">validateData</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">convergenceTol</span><span class="o">=</span><span class="default_value">0.001</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/mllib/regression.html#LinearRegressionWithSGD.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.regression.LinearRegressionWithSGD.train" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Train a linear regression model using Stochastic Gradient |
| Descent (SGD). This solves the least squares regression |
| formulation</p> |
| <blockquote> |
| <div><p>f(weights) = 1/(2n) ||A weights - y||^2</p> |
| </div></blockquote> |
| <p>which is the mean squared error. Here the data matrix has n rows, |
| and the input RDD holds the set of rows of A, each with its |
| corresponding right hand side label y. |
| See also the documentation for the precise formulation.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 0.9.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>data</strong><span class="classifier"><a class="reference internal" href="pyspark.RDD.html#pyspark.RDD" title="pyspark.RDD"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD</span></code></a></span></dt><dd><p>The training data, an RDD of LabeledPoint.</p> |
| </dd> |
| <dt><strong>iterations</strong><span class="classifier">int, optional</span></dt><dd><p>The number of iterations. |
| (default: 100)</p> |
| </dd> |
| <dt><strong>step</strong><span class="classifier">float, optional</span></dt><dd><p>The step parameter used in SGD. |
| (default: 1.0)</p> |
| </dd> |
| <dt><strong>miniBatchFraction</strong><span class="classifier">float, optional</span></dt><dd><p>Fraction of data to be used for each SGD iteration. |
| (default: 1.0)</p> |
| </dd> |
| <dt><strong>initialWeights</strong><span class="classifier"><a class="reference internal" href="pyspark.mllib.linalg.Vector.html#pyspark.mllib.linalg.Vector" title="pyspark.mllib.linalg.Vector"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.mllib.linalg.Vector</span></code></a> or convertible, optional</span></dt><dd><p>The initial weights. |
| (default: None)</p> |
| </dd> |
| <dt><strong>regParam</strong><span class="classifier">float, optional</span></dt><dd><p>The regularizer parameter. |
| (default: 0.0)</p> |
| </dd> |
| <dt><strong>regType</strong><span class="classifier">str, optional</span></dt><dd><p>The type of regularizer used for training our model. |
| Supported values:</p> |
| <ul class="simple"> |
| <li><p>“l1” for using L1 regularization</p></li> |
| <li><p>“l2” for using L2 regularization</p></li> |
| <li><p>None for no regularization (default)</p></li> |
| </ul> |
| </dd> |
| <dt><strong>intercept</strong><span class="classifier">bool, optional</span></dt><dd><p>Boolean parameter which indicates the use or not of the |
| augmented representation for training data (i.e., whether bias |
| features are activated or not). |
| (default: False)</p> |
| </dd> |
| <dt><strong>validateData</strong><span class="classifier">bool, optional</span></dt><dd><p>Boolean parameter which indicates if the algorithm should |
| validate data before training. |
| (default: True)</p> |
| </dd> |
| <dt><strong>convergenceTol</strong><span class="classifier">float, optional</span></dt><dd><p>A condition which decides iteration termination. |
| (default: 0.001)</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
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